Through the use of systems such as GPS, satellite navigation has become a critical element of society and economy. Global Navigation Satellite Systems (GNSS) involve the transmission of radionavigation signals to (typically, but not exclusively, ground-based) receivers where they are processed and used for ranging purposes, or to calculate a position, velocity, time (PVT) solution.
Many modern Global Navigation Satellite System (GNSS) signals broadcast composite Code Division Multiple Access (CDMA) signals which use an Offset Carrier Modulation (OCM). These signals incorporate varying numbers of baseband components and a range of sub-carriers. Examples include (i) Binary Phase-Shift Keyed (BPSK) baseband signals modulated by sinusoidal sub-carriers resulting in OCM signals, (ii) BPSK baseband signals modulated by square-wave sub-carriers, resulting in Binary Offset Carrier (BOC) signals, and (iii) Quadrature Phase-Shift Keyed (QPSK) signals using sinusoidal sub-carriers. In general, these signals exhibit a symmetric Power Spectrum Density (PSD) with little power located at the center frequency and two main lobes, located at either side of the signal center frequency, which contain the majority of the signal power.
This spectral shape, coupled with the autocorrelation properties of the baseband CDMA components, yields a signal which can provide high accuracy ranging. The autocorrelation function of such signals is typically steep and exhibits numerous zero-crossings. As the ranging accuracy provided by these signals is directly related to the signal autocorrelation function, these signals are often tuned to have a high slope near around the zero-offset point. However, this comes at a cost, which is generally manifest as difficulties experienced by the receiver in the initial signal acquisition phase, and when strong multipath conditions (involving reflected signals) prevail.
As many GNSS signals are broadcast from each satellite, it is not uncommon that the center-frequency of offset-carrier modulated signal coincides with a second signal which either has been modulated with either (a) no sub-carrier, or (b) a sub-carrier of a low frequency.
To demonstrate the challenges of processing offset-carrier modulated signals, an example OCM signal configuration will now be discussed, with reference to FIGS. 1 to 5 (PRIOR ART).
The particular signal chosen for illustration purposes is an OCM which uses a square-wave sub-carrier, typically termed a BOC modulation, with a primary code rate of (2.5×1.023) Mcps and a cosine-phased sub-carrier rate of (15×1.023) MHz. The composite modulation, denoted BOCc(15,2.5) has a normalized PSD and autocorrelation function depicted in FIGS. 1 and 2, respectively.
Specifically, the signal of interest (a down-converted and digitized version of the radionavigation signal received at the receiver's antenna) is denoted sA (t) which is modelled as follows:sA(t)=√{square root over (2PA)} cos(2πFAt+θA)CA(t)SCA(t),  (1)where PA denotes the nominal received power, FA is the nominal broadcast center frequency, CA (t) is the CDMA spreading sequence, and SCA (t) is the square-wave sub-carrier. Estimates of various signal parameters including, for example, FA and θA, are generally extracted via correlation of the received signal and a local replica, the result, typically termed the correlator value and denoted YA (f, τ, θ), is computed via:
                                                        Y              A                        ⁡                          (                              f                ,                τ                ,                θ                            )                                =                                    1                              T                I                                      ⁢                                          ∫                t                                  t                  +                                      T                    I                                                              ⁢                                                                    s                    A                                    ⁡                                      (                    t                    )                                                  ⁢                                  exp                  ⁡                                      (                                          -                                              j                        ⁡                                                  (                                                                                    2                              ⁢                              π                              ⁢                                                                                                                          ⁢                              f                              ⁢                                                                                                                          ⁢                              t                                                        +                            θ                                                    )                                                                                      )                                                  ⁢                                                      C                    A                                    ⁡                                      (                                          t                      +                      τ                                        )                                                  ⁢                                                      SC                    A                                    ⁡                                      (                                          t                      +                      τ                                        )                                                  ⁢                                                                  ⁢                dt                                                    ,                            (        2        )            where TI, often termed the pre-detection integration period is generally of short duration, perhaps some milliseconds, and is generally chosen in accordance with the period of sCDMA spreading sequence, CA.
One feature of this modulation that can be challenging for a receiver is the presence of multiple, so-called, side-peaks in the autocorrelation function, leading to acquisition ambiguity. When a receiver attempts to acquire such a signal, it typically implements a search across the code-delay τ, striving to detect the largest autocorrelation peak. Ideally this will correspond to the alignment between the received signal and the local replica signal. A problem is that, due the large relative magnitude of the adjacent peaks, both positive and negative, of the BOCc(15, 2.5) autocorrelation function, the presence of thermal noise interference can lead a receiver to identify one of the adjacent local-maxima as the maximum value. In terms of receiver operation, this can correspond to a bias in the measured range and, thereby, degrade positioning accuracy.
As a demonstration of this particular problem, we consider that the signal has been acquired by detecting and tracking each of its components parts, the upper and lower side-lobes, separately. This corresponds to the individual or joint acquisition of one or both of the BPSK signals centered at Fc±(15×1.023) MHz. Given this coarse acquisition estimate, a receiver may begin to track the BPSK signals to refine the delay and frequency alignment and, subsequently, attempt a fine acquisition of the composite BOCC(15, 2.5) signal. In doing so, the receiver may populate an acquisition search space, across the delay uncertainty. Typically this search will have a finite range and finite delay resolution, such that the uncertainty space occupies samples of the autocorrelation function, depicted in FIG. 2. As an example, we assume that the receiver may not be coherently tracking the signal, such that there may be a phase uncertainty and, therefore, might implement a non-coherent detection scheme.
The decision variable (|YA|2) produced by examining the square magnitude of a complex correlation YA between a received signal and a local replica, having perfect frequency synchronization, unaligned phase and a range of code-delays is presented in FIG. 3. When attempting to align the local replica signals with the received GNSS signals, the receiver may observe a range of code delays around the current best estimate. This range will depend on the uncertainty of the current code delay estimate.
As an example of this problem, FIG. 4 depicts the probability of choosing the correct code-delay when examining a range of correlator values, spaced at 1 meter intervals across a range extending ±30 meters for a selection of received C/N0 values. While in the absence of thermal noise, selection of the appropriate code delay will be trivial, upon inspection of FIG. 4 it is clear that the performance may degrade rapidly with reduced signal quality. In particular, and as seen also in FIG. 3, it is noteworthy that the local maxima immediately adjacent to the (central) global maximum have relative magnitudes of almost 0.8.
Results are presented in FIG. 4 wherein it is clear that a receiver will experience significant difficulty in acquiring the appropriate code delay under weak signal conditions. Of course, the results presented here correspond only to the case where a receiver integrates over a period of TI=1 ms. The performance can be improved by extending the integration period, however, this period is ultimately limited by the signal design and receiver operating conditions.
One further challenge experienced by receivers processing BOC signals is that of false-lock of the code tracking architecture: multiple stable lock points. Generally, a receiver will form some sort of discriminator to estimate misalignment spreading sequence, CA, and secondary code, SCA between the received signal and the local replica. This is typically done by generating correlator values that are equally spaced, early and late, relative to the best estimate of the code delay. Differencing these early and late correlator values, respectively denoted YE and YL, can generate the code-delay error estimate.
Depending on the receiver design, it may or may not coherently track the phase of the received signal. In cases where the received signal is tracked a coherent estimate can be made and if the signal phase is not tracked or if it is likely to be misaligned, then a non-coherent estimate can be made. For example, basic coherent and non-coherent delay estimates can be made via:ecoh=Acoh({YE}−{YL})  (3)enon-coh=Anon-coh(|YE|2−|YL|2)  (4)where Acoh and Anon-coh are normalizing gains, generally a function of both the received signal strength, the signal modulation type and the relative spacing between the early and late correlator values; and {x} denotes the real part of a complex value x. Functions ecoh and enon-coh generally produce an error estimate that is proportional to the true delay for a small range of delay values, centered around zero. A problem is that, outside this range, the error function can exhibit positive-sloped zero-crossings at which a code tracking scheme may experience a stable lock. These, so called, false-lock points can lead to biases in the measured range. The more complex the signal modulation, the greater the number of these false-lock points. Also, in the case of the BOC modulation, the non-coherent case will exhibit more false-lock points than the coherent case.
FIG. 5(a) depicts the coherent code error estimate, and FIG. 5(b) depicts the non-coherent code error estimate, of a BOCc(15, 2.5) signal given an early-to-late correlator spacing of 5 m. In the coherent case, the modulation results in twelve stable lock points which do not correspond to the true signal delay, although perhaps only ten of these are significant. More troubling is that in the non-coherent case this number increases to twenty-four and the range, over which the error estimate is proportional to the true error, shrinks by a factor of two. The implications of this are that a receiver, when operating in non-deal conditions, such as fading or high-dynamics, may struggle to converge to the correct stable lock point, resulting in biased range measurements.
US2014119392A discloses a receiver for receiving a composite signal transmitted from a satellite, such as a navigation satellite (e.g., a multiplexed binary offset carrier signal or pilot component of the L1C signal for the Global Positioning System (GPS)) the receiver being capable of at least partially decoding the received composite signal that is received. In one embodiment, the received composite signal is from a Galileo-compatible navigation satellite or Global Positioning System satellite. In one embodiment, the received composite signal refers to a first binary offset carrier signal that is multiplexed with a second binary offset carrier signal.
EP2402787A1 discloses a GNSS receiver that can perform correlation processing on a positioning signal phase-modulated by a CBOC signal. A correlation processing module performs correlation processing between a baseband signal and a BOC(1, 1) replica code to output a BOC(1, 1) correlation data, and also performs correlation processing between the baseband signal and a BOC(6, 1) replica code to output a BOC(6, 1) correlation data.